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Zapier's first API

Hacker News

Zapier's first API

Hey HN! We launched Zapier way back in 2012 on HN: https://news.ycombinator.com/item?id=4138415 and thought we'd return home to announce something special and hopefully exciting :) We are trying to finally live up to the "API" in our name with Zapier's first universal API: Natural Language Actions – https://zapier.com/l/natural-language-actions API docs – https://nla.zapier.com/api/v1/docs (to be fair, we have published APIs before that can access Zapier data, but never before one devs can use to directly call the 5k+ apps / 20k+ actions on our platform) For example, you can use the API to: * Send messages in Slack * Retrieve a row in a Google Sheet * Draft a reply in Gmail * ... and thousands more actions with one universal API We optimized NLA for use cases that receive user input in natural language (think chatbots, assistants, or any product/feature using LLMs) -- but not strictly required! Folks have asked for an API for 10 years and I've always been slightly embarrassed we didn't have one. We hesitated because we did not want to pass along our universe of complexity to end devs. With the help of LLMs we found some cool patterns to deliver the API we always wanted. My co-founder/CTO Bryan did an interview with Garry on YC blog with more details: https://www.ycombinator.com/blog/building-apis-for-ai-an-int... We also published a LangChain integration to show off some possibilities: * Demo: https://www.youtube.com/watch?v=EEK_9wLYEHU * Jupyter notebook: https://github.com/hwchase17/langchain/blob/master/docs/modules/utils/examples/zapier.ipynb We know the API is not perfect but we're excited and eager for feedback to help shape it.

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235points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, google, apps · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
83%83% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps, google, way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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